Preconditioning Human Adipose-Derived Stromal Cells on Decellularized Adipose Tissue Scaffolds Within a Perfusion Bioreactor Modulates Cell Phenotype and Promotes a Pro-regenerative Host Response
Bibliographic record
Abstract
Cell-based therapies involving the delivery of adipose-derived stromal cells (ASCs) on decellularized adipose tissue (DAT) scaffolds are a promising approach for soft tissue augmentation and reconstruction. Our lab has recently shown that culturing human ASCs on DAT scaffolds within a perfusion bioreactor prior to implantation can enhance their capacity to stimulate in vivo adipose tissue regeneration. Building from this previous work, the current study investigated the effects of bioreactor preconditioning on the ASC phenotype and secretory profile in vitro , as well as host cell recruitment following implantation in an athymic nude mouse model. Immunohistochemical analyses indicated that culturing within the bioreactor increased the percentage of ASCs co-expressing inducible nitric oxide synthase (iNOS) and arginase-1 (Arg-1), as well as tumor necrosis factor-alpha (TNF-α) and interleukin-10 (IL-10), within the peripheral regions of the DAT relative to statically cultured controls. In addition, bioreactor culture altered the expression levels of a range of immunomodulatory factors in the ASC-seeded DAT. In vivo testing revealed that culturing the ASCs on the DAT within the perfusion bioreactor prior to implantation enhanced the infiltration of host CD31 + endothelial cells and CD26 + cells into the DAT implants, but did not alter CD45 + F4/80 + CD68 + macrophage recruitment. However, a higher fraction of the CD45 + cell population expressed the pro-regenerative macrophage marker CD163 in the bioreactor group, which may have contributed to enhanced remodeling of the scaffolds into host-derived adipose tissue. Overall, the findings support that bioreactor preconditioning can augment the capacity of human ASCs to stimulate regeneration through paracrine-mediated mechanisms.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".